A COMMERCIAL OFF-THE-SHELF IMPLEMENTATION OF AN ANALOG NEURAL COMPUTER

Abstract

For most applications, analog electrical circuit implementations of continuous-valued neural networks have been abandoned in favor of digital simulations. This is not surprising, as both precision and accuracy can be more easily ensured in digital computers. Still, because they use far fewer transistors and support components, analog circuits can still be orders of magnitude smaller than their digital simulations. In some application, like micro-robotics and embedded control, one might be willing to tolerate less accuracy and precision for the size and power benefits. One would not under any condition, however, tolerate significant behavioral mismatches between the differential equation and electrical circuit forms of the neural networks in question. In this paper, we will present a design for an analog neural computer that embodies the commonly used continuous time recurrent neural network. We will show that the computer possesses excellent behavioral congruence to the differential equation form even in the presence of significant practical compromises. We will also discuss the implications of this work for both practical Commercial, Off-The-Shelf (COTS) and Application-Specific Integrated Circuit (ASIC) devices.